2021/08/06 by Kenny Peng, Arunesh Mathur, Peng, Kenny +3 · 1 voice · 3 citations
Computer Science · Social Sciences · #Face recognition and analysis #Privacy-Preserving Technologies in Data #Ethics and Social Impacts of AI
paper · pdf · doi:10.48550/arxiv.2108.02922
Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets -- Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset.